Analytical methods for inferring functional effects of single base pair substitutions in human cancers

Abstract

Cancer is a genetic disease that results from a variety of genomic alterations. Identification of some of these causal genetic events has enabled the development of targeted therapeutics and spurred efforts to discover the key genes that drive cancer formation. Rapidly improving sequencing and genotyping technology continues to generate increasingly large datasets that require analytical methods to identify functional alterations that deserve additional investigation. This review examines statistical and computational approaches for the identification of functional changes among sets of single-nucleotide substitutions. Frequency-based methods identify the most highly mutated genes in large-scale cancer sequencing efforts while bioinformatics approaches are effective for independent evaluation of both non-synonymous mutations and polymorphisms. We also review current knowledge and tools that can be utilized for analysis of alterations in non-protein-coding genomic sequence.

Fig. 1

Genomic regions, which are subject to functional alteration through single-nucleotide substitutions. Select computational tools that could be used for mapping or analysis of the various kinds of sequence elements are listed under each category. Methods for analysis of amino acid substitutions are roughly separated into those that incorporate protein structure information or those that are purely sequence based